After the search, we fully train the best-performing GAN \((G^*,D^*)\)  from scratch. For the CIFAR-10 dataset, the batch size and learning rate are the same as the search stage, but the total number of training epochs is 600. For the STL-10 dataset, the batch size and the learning rate are 128 and 0.0003 for the generator, and 64 and 0.0002 for the discriminator, respectively. Following the previous NAS-GAN works [1]}, [2]}, we generate 50,000 images to calculate IS and FID metrics.
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